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Comparative Metagenomic and Metatranscriptomic Analysis of Hindgut Paunch Microbiota in Wood- and Dung-Feeding Higher Termites

  • Shaomei He,

    Affiliations Energy Biosciences Institute, University of California, Berkeley, California, United States of America, US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America

  • Natalia Ivanova,

    Affiliations Energy Biosciences Institute, University of California, Berkeley, California, United States of America, US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America

  • Edward Kirton,

    Affiliation US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America

  • Martin Allgaier,

    Affiliations US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America, Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin Center for Genomics in Biodiversity Research, Berlin, Germany

  • Claudia Bergin,

    Affiliation Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden

  • Rudolf H. Scheffrahn,

    Affiliation Fort Lauderdale Research and Education Center, University of Florida, Davie, Florida, United States of America

  • Nikos C. Kyrpides,

    Affiliations Energy Biosciences Institute, University of California, Berkeley, California, United States of America, US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America

  • Falk Warnecke,

    Affiliations Energy Biosciences Institute, University of California, Berkeley, California, United States of America, US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America

  • Susannah G. Tringe,

    Affiliation US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America

  • Philip Hugenholtz

    Affiliations Energy Biosciences Institute, University of California, Berkeley, California, United States of America, US Department of Energy (DOE) Joint Genome Institute, Walnut Creek, California, United States of America, Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences & Institute for Molecular Bioscience, The University of Queensland, St Lucia, Queensland, Australia


Termites effectively feed on many types of lignocellulose assisted by their gut microbial symbionts. To better understand the microbial decomposition of biomass with varied chemical profiles, it is important to determine whether termites harbor different microbial symbionts with specialized functionalities geared toward different feeding regimens. In this study, we compared the microbiota in the hindgut paunch of Amitermes wheeleri collected from cow dung and Nasutitermes corniger feeding on sound wood by 16S rRNA pyrotag, comparative metagenomic and metatranscriptomic analyses. We found that Firmicutes and Spirochaetes were the most abundant phyla in A. wheeleri, in contrast to N. corniger where Spirochaetes and Fibrobacteres dominated. Despite this community divergence, a convergence was observed for functions essential to termite biology including hydrolytic enzymes, homoacetogenesis and cell motility and chemotaxis. Overrepresented functions in A. wheeleri relative to N. corniger microbiota included hemicellulose breakdown and fixed-nitrogen utilization. By contrast, glycoside hydrolases attacking celluloses and nitrogen fixation genes were overrepresented in N. corniger microbiota. These observations are consistent with dietary differences in carbohydrate composition and nutrient contents, but may also reflect the phylogenetic difference between the hosts.


As some of the most abundant and efficient lignocellulose decomposers on the planet, termites tremendously impact lignocellulose biorecycling, and rank as one of the most important “ecosystem engineers”. Termite success is enabled by their gut microbial symbionts which participate in lignocellulose depolymerization, perform subsequent fermentation, and provide important nutrients to the hosts [1]. Unlike the phylogenetically distinct lower termites, the higher termites (family Termitidae) lack protozoan symbionts and their hindguts are highly compartmentalized with different physicochemical conditions and microbial communities to collaboratively accomplish lignocellulose degrading and fermentation functions [2], [3], [4], [5]. The third proctodeal segment, P3, also called the paunch, is the largest hindgut compartment with the highest microbial cell count and concentration of fermentation products, and therefore has been suggested to be the major microbial bioreactor in the higher termite gut [5]. Consistent with this suggestion, a metagenomic analysis of the P3 microbiota of wood-feeding Nasutitermes sp. workers collected in a Costa Rican rainforest revealed a rich diversity of carbohydrate active enzymes as well as genes encoding other functions important to termite biology [6], revealing the genetic potential of microbial symbionts in lignocellulose degradation.

Although termites are perceived as mainly wood consumers, particularly as pests of structural lumber in the tropical and subtropical regions, many termite species feed on other resources, such as soil, grass, and litter. Some termites, especially in the genus Amitermes, also opportunistically and preferentially forage on herbivore dung, such as from cattle, when it is available [7], [8]. Cow dung contains undigested fibrous plant biomass, cattle endogenous secretions and microbial debris and is thus richer in nutrients than wood. Utilization of cow dung by termites facilitates the return of nutrients contained in dung to soil, impacting ecosystem nutrient cycling and maintaining the fertility and productivity of tropical soil [9].

A largely open question is whether termites harbor different microbial symbiont populations with specialized functionalities geared towards different feeding regimens, as has been suggested for other animals [10]. Therefore the aim of this study was to determine if system-specific differences exist between hindgut paunch microbiota from higher termites with different diets. Two higher termite species from the Termitidae family, Amitermes wheeleri and Nasutitermes corniger, belonging to subfamily Termitinae and Nasutitermitinae respectively, were included in our study. A. wheeleri can feed on various food sources (polyphagous), including not only wood but also dead grasses, small shrubs and herbivore dung, and were shown to have a preference toward dung over mesquite wood [11]. N. corniger are strict wood-feeders, consuming dry, wet, or partially decayed hardwoods or softwoods [12]. A. wheeleri workers were collected from a solar cured cow pie within which they had hollowed out chambers. Our presumption is that these termites were foraging on cow dung at the time of sampling (namely cow dung-feeding A. wheeleri). The N. corniger sample was collected from a laboratory colony fed with dry wood (namely laboratory wood-feeding N. corniger to distinguish from previous Costa Rican wood-feeding Nasutitermes sp. [6]). As in the previous metagenomic analysis of Nasutitermes [6], we focused our study on the largest microbial reservoir, the P3 segment, in particular the lumen fluid fraction to avoid host nucleic acids, while acknowledging that P3 epithelium-associated microbiota and other gut segments may also be important in lignocellulose degradation and nutrient utilization. We first evaluated microbial community composition using 16S rRNA gene pyrotag analysis. We then compared community functions based on metagenomic and metatranscriptomic analyses. Our analysis on N. corniger corroborates and extends the findings previously obtained from Costa Rican Nasutitermes sp. [6], while the comparison between A. wheeleri and N. corniger highlighted a number of key differences. We discovered that the P3 lumen microbial communities are phylogenetically distinct yet perform similar core termite symbiont functions such as lignocellulose degradation and homoacetogenesis, but also have distinct functionalities reflecting dietary differences, that suggest microbial adaptations to the chemical composition of dung.

Results and Discussion

Microbial community composition

Pyrosequencing of PCR amplicons from the V8 region of the 16S rRNA gene (pyrotag analysis) was used for community taxonomic profiling. Archaeal sequences comprised only a tiny proportion (<0.2%) of the profiled hindgut paunch lumen communities, indicating that bacteria were the major microbial symbionts in these samples. Rarefaction curves and Shannon diversity index based on operational taxonomic units (OTUs) defined by 97% sequence identity indicated that A. wheeleri microbiota has about three-fold higher species richness than the laboratory N. corniger microbiota (Figure S1a). It is not clear whether this indicates an adaption of A. wheeleri microbial community to the higher complexity of their diet or a decrease in species diversity in N. corniger community caused by the laboratory rearing.

The N. corniger hindgut paunch microbiota was dominated by Spirochaetes, particularly members of the genus Treponema, followed by Fibrobacteres, together accounting for ∼90% of the bacterial community (Figure 1 & Figure S1b). Of the 16S rRNA gene pyrotag sequences classified as Fibrobacteres, about one third belong to Fibrobacteres subphylum 2 and two-thirds to Termite Group 3 (TG3); both groups having first been identified in wood-feeding higher termites (Microcerotermes sp. and Nasutitermes takasagoensis) [13], [14]. TG3 has been proposed as an independent bacterial phylum [13], however, we chose to assign this group to the Fibrobacteres based on its mostly consistent phylogenetic affiliation with this phylum. A genome-based analysis will be required to resolve whether TG3 is an independent phylum or a class within the Fibrobacteres [1]. The Spirochetes- and Fibrobacteres-dominated community profile is consistent with previous culture-independent reports of Nasutitermes P3 hindgut microbiota including a Costa Rican colony [6] and the same laboratory-maintained colony analyzed using a different region of the 16S rRNA gene (variable regions V3–V4) [5]. By contrast, the A. wheeleri hindgut largely comprised members of the Firmicutes phylum (47%), especially Clostridia, followed by Spirochaetes (25%). Synergistetes and Deltaproteobacteria were also found at moderate abundance (Figure 1 & Figure S1b). Fibrobacteres (including Subphylum 2 and TG3) were only detected at a very low abundance (∼1%) in A. wheeleri. This is consistent with a previous suggestion that these lineages are only abundant in wood-feeding higher termites, as they are largely absent in other feeding guilds of higher termites and in lower termites [14], suggesting that both host phylogeny and diet play a role in determining Fibrobacteres/TG3 distribution in termite hindguts.

Figure 1. Phylogenetic affiliation, relative abundance and habitat distribution of OTUs that are >0.5% of total bacterial community.

The bubble size represents the relative abundance of each OTU, and bubble color indicates the types of habitats where their closest relatives in the greengenes database were found. OTUs marked red were exclusively found in higher termites, orange OTUs were restricted to termites, including both higher and lower termites, green OTUs were also found in the guts and feces of other animals (e.g. cow, goat and elephant). Blue OTUs were found in other anoxic environments, such as anaerobic digesters.

A potential diet-associated difference in bacterial community composition has also previously been noted between two termite species [15]: a wood-feeding lower termite, Reticulitermes speratus, dominated by Spirochaetes, mostly belonging to the genus Treponema [16], and a soil-feeding higher termite, Cubitermes, dominated by Firmicutes, mostly belonging to the class Clostridia [17]. Like cow dung, the litter-derived lignocellulose-containing soil is rich in nitrogenous compounds of microbial origin. The enrichment of Clostridia in two higher termite genera on relatively nitrogen-rich diets and enrichment of treponemes in two distantly related termite genera on nitrogen-poor diets supports the hypothesis that the observed community dissimilarity in our study can be partly attributed to the difference in dietary nutrients. However, an influence of host phylogeny on hindgut community structure should not be ruled out. A number of previous studies have revealed correlations between termite gut microbiota and their hosts, indicating an important role for co-evolution in the make-up of symbiont microbial communities [13], [14], [18]. It is very likely that both host phylogeny and diet influence termite symbionts, however their individual contributions cannot be explicitly resolved due to the limited number of termite species and host-diet combinations investigated in our study.

Habitat distribution of the termite gut OTUs can provide clues about the evolutionary acquisition of different microbial species. Habitat distribution was estimated for each OTU based on publicly available 16S rRNA gene sequences belonging to that OTU. Most Treponema and Fibrobacter-like OTUs characteristic of wood-feeding species have only been identified in termite guts, and are phylogenetically distinct from other members of the Spirochaetes and Fibrobacteres phyla found in different environments. This supports a previously suggested long-term co-evolution of these species with their insect hosts with increasing specialization towards lignocellulose degradation [19], [20]. By contrast, Firmicutes OTUs enriched in polyphagous A. wheeleri were also represented in other anoxic environments, many of which are associated with animal guts and feces, suggesting that some of these microbial populations may have been more recently acquired by termites in response to changes in host diet (see below).

Gene-centric analysis of community metabolism

We performed metagenomic analyses in order to discover functional differences between the A. wheeleri and N. corniger P3 lumen microbiota. Metagenome data were generated with a combination of Sanger and 454 pyrosequencing; Table S1 lists the summary results of metagenome sequencing, assembly and annotation. Phylogenetic composition of metagenomes was evaluated by sequence homology-based taxonomic assignment of genes belonging to a total of 38 phylogenetic marker gene families (COGs, listed in Table S2) (Figure S1c) and by all metagenome genes with function prediction (Figure S1d) respectively. Archaeal sequences accounted for less than 0.1% of these genes in agreement with the pyrotag data. At the phylum level, bacterial community profiles estimated using marker COGs were largely consistent with the pyrotag profiles (Figure S1b).

We conducted gene-centric analysis [21] to compare the metagenomes based on functional units of pfam, COG, or COG functional categories. We included the Costa Rican wood-feeding Nasutitermes metagenome [6] as a reference dataset in the analysis with the expectation that it would functionally resemble the laboratory-maintained N. corniger and help to highlight significant metabolic differences with the dung-feeding A. wheeleri metagenome. On a global scale, the three termite P3 metagenomes shared higher similarities in functional profiles to each other than to other animal guts or lignocellulose-degrading ecosystems, with the two wood-feeding Nasutitermes metagenomes exhibiting the highest similarity (Figure S2) despite the different sequencing depths and technologies used in these studies. Many consistencies among the three termite P3 metagenomes were also observed as compared to other metagenomes and genomes in IMG/M (Figure S3a, b), indicating functional commonalities in the termite hindgut paunch microbiota. In particular, cell motility and associated chemotaxis are overrepresented which are required by microorganisms to respond to steep physicochemical gradients found in the termite hindgut [2], [3], [4], [5], [22].

We next analyzed the similarities and differences in expression profiles of the A. wheeleri and laboratory-maintained N. corniger termite P3 microbiomes using metatranscriptomics. To recover expressed genes that may not have been detected in the metagenomes, we performed a de novo assembly of metatranscriptome reads, which was then co-assembled with the metagenome to generate a more complete reference to map cDNA reads for gene expression (Figure S4). A summary of metatranscriptome sequencing, de novo assembly, annotation and read mapping results is presented in Table S3. Sequence homology-based taxonomic assignment of functional genes from metatranscriptome assemblies (Figure S1e) indicates that the most abundant transcripts were from Firmicutes and Spirochaetes for A. wheeleri and N. corniger respectively consistent with their pyrotag and metagenome profiles (Figure S1b,c,d). The transcript distribution among COG categories supports the importance of cell motility and carbohydrate transport and metabolism in the A. wheeleri and N. corniger P3 microbiota as they were the most highly expressed functional categories (Figure S5).

More specific individual functions and pathways that are differentially represented between the A. wheeleri and N. corniger metagenomes (Table S4) and between their metatranscriptomes (Table S5) were identified. The key metabolic differences are schematically presented in Figure 2 and discussed hereafter.

Figure 2. Key metabolic differences between cow dung- and wood-feeding termites based on gene and transcript abundance profiles.

In this schematic summary, intracellular and extracellular reactions are separated by a cell membrane, but these reactions do not necessarily all occur in one cell. Green or red thin lines (including rectangle outlines) indicate genes more abundant in the A. wheeleri or N. corniger metagenome, respectively. Green or red thick lines (including rectangle highlights) indicate transcripts more abundant in the A. wheeleri or N. corniger metatranscriptome, respectively. Black lines indicate equal representation in both termite genera.

Lignocellulose degradation

Cellulose, hemicellulose and lignin are the three major components of plant cell walls. In higher termites, both hosts and their gut symbionts participate in lignocellulose degradation [1]. However in our study we only focused on the contribution of symbiont microbial communities. The previous metagenomic study of the P3 lumen fluid of Nasutitermes did not identify lignin degradation genes [6]. Similarly, our current analyses did not find genes from known lignin degradation pathways in the P3 microbiota of either termite species. This may be partly due to the oxygen status in P3 (microoxic periphery around an anoxic center [2], [5], [22]), which may not support the known aerobic lignin degradation pathways. Early reports of enzymatic lignin degradation during the passage through the termite gut were somewhat ambiguous (see reviews [1], [23]). Since celluloses and hemicelluloses need to be disassociated from lignin to increase accessibility for efficient enzymatic degradation, lignin modification or partial degradation (if not complete mineralization) is expected, and this functionality may be provided by gut segments not investigated in our study or by the termite host. For example, the P1 segment has a high alkalinity in several higher termite species [2], [3], [5], and alkaline pretreatment disrupts lignin structure in industrial lignocellulose degradation [24]. Further, microbial community composition varies among different compartments of higher termites [17], [25], [26], and specifically for N. corniger, Köhler et al. [5] have demonstrated notable differences in microbial communities among different gut segments. Therefore, other gut segments, particularly the more aerobic regions, may harbor populations capable of lignin degradation. In addition, the contribution of higher termite host to lignin degradation, although still remain unclear, cannot be ruled out.

In contrast to the absence of identifiable lignin degradation genes, glycoside hydrolases (GHs), necessary for cellulose and hemicellulose degradation, were highly represented and expressed in the termite hindguts (Table S6). The relative abundance of GH families in the P3 metagenomes is listed in Table 1 according to their broad functional categories. The screening of a metagenomic fosmid library for lignocellulolytic activities identified GH5, 8, 10 and 11 gene families from a wood-feeding higher termite (Microcerotermes sp.) hindgut microbial community [27]. Our wood-feeding N. corniger metagenome also had abundant genes in these GH families, and compared to A. wheeleri, N. corniger had higher relative abundances of cellulases (GH5, 9 and 45) and endohemicellulases (GH 8, 10, 11, 26 and 53) which attack celluloses and main chains of hemicelluloses (Table S4). A cluster heatmap based on the abundance of individual GHs relative to total GHs showed that the two Nasutitermes spp. shared the highest similarity in GH profile; and the three termite P3 metagenomes exhibited higher similarities to each other than to other lignocellulose-degrading systems, including a compost adapted to switchgrass [28], tammar wallaby foregut [29], wild panda gut [30] and cow rumen [31], [32] (Figure S6). Interestingly, microbiota associated with switchgrass incubated in a cow rumen [32] had a high similarity in GH profile to A. wheeleri, while showing a lower similarity to cow rumen fed with forage grass and legume [31]. In addition to the differences in sequencing platform and efforts, and in substrates (switchgrass versus forage grass and legume), the discrepancy might be partly due to the long incubation time (72 hrs) of switchgrass, which may allow the establishment of microbes capable of attacking more recalcitrant backbones in the diet, compared to the initial colonization of fiber that preferentially used more easily accessible sugars in the side chains [31].

Table 1. Inventory of putative glycoside hydrolases (GHs) in plant cell wall degradation in termite hindguts and other environmentsa.

Debranching enzymes that cleave side chains of hemicelluloses were overrepresented in A. wheeleri as compared to N. corniger metagenomes. The most abundant debranching enzymes in A. wheeleri were arabinofuranosidases of GH family 51, which catalyze the hydrolysis of α-1,2 and α-1,3-arabinofuranosidic bonds in arabinose-containing side chains of hemicelluloses, such as (glucurono)arabinoxylans, the major form of hemicellulose in grasses but not wood [33]. Arabinofuranosidases and rhamnosidases (GH78) were also abundant in grass-decomposing systems: switchgrass-adapted compost [28], cow rumen [31], [32] and tammar wallaby foregut [29], suggesting that these enzymes are associated with grass hemicelluloses. As the cattle at the sampling site primarily consumed range grasses, the diet of A. wheeleri is ultimately derived from grass. In general, grass has a higher hemicellulose content than wood, and the major form is (glucurono)arabinoxylans, compared to xyloglucan and glucuronoxylan that dominate in wood [33], [34]. Therefore, the higher abundances of these two GHs (51 and 78) in A. wheeleri than in the two Nasutitermes species is consistent with the grass-origin of cow dung and/or the polyphagous nature of A. wheeleri that also feed on grass. Overall, these observations support the inference that diet, specifically carbohydrate composition, drives the GH profile in the lignocellulose-degrading gut environments [31].

In the metatranscriptomes, carbohydrate transport and metabolism was the second most highly expressed COG function category for both termites (Figure S5). Although a wide array of GHs was identified in metagenomes, not all of these GHs were detected in the metatranscriptomes (Figure S7), in part due to the low recovery of functional genes in metatranscriptomes as compared to rRNA (Table S3), but also likely linked to lower expression of these genes, highlighting the limitation of solely relying on metagenomes to infer community functions. In particular, the expression of debranching enzymes and oligosaccharide degrading enzymes was largely undetected, compared to enzymes targeting celluloses and main chains of hemicelluloses. GH5 (cellulases) and GH11 (xylanases) had the highest expression levels among all GHs in N. corniger and A. wheeleri metatranscriptomes respectively. Compared to wood, grass in general has a higher content of xylan [34], consistent with the high expression of xylanase genes in A. wheeleri. Notably, GH11 was not the most abundant GH in either metagenome, and the top ten highly expressed GH11 genes were identified solely from the de novo assembled metatranscriptome. By homology, seven of those ten can be assigned to Firmicutes. Without including the de novo assembled metatranscriptome in the reference for mapping the metatranscriptome reads, we would have overlooked the expression of these genes. This highlights the value of de novo metatranscriptome assembly in retrieving highly expressed genes that are present at low relative abundance in metagenomes.

Cohesins and dockerins, key components of cellulosomes, were detected in A. wheeleri while being largely undetected in both Nasutitermes species. These cohesin and dockerin genes were most similar to those from Clostridia, many species of which are known cellulosome producers, and this is consistent with the dominance of Clostridia in A. wheeleri. By contrast, a protein family TIGR02145, markedly abundant in Costa Rican Nasutitermes sp. [6], was also abundant in the laboratory N. corniger but much less so in A. wheeleri. This family primarily consists of Fibrobacter-specific extracytoplasmic proteins, and is hypothesized to be an analog of cohesins [6]. The different abundances of these protein families primarily reflect the difference in microbial community composition, and suggest that a similar function (substrate binding in this case) was provided by different microbial populations.

Nitrogen metabolism and dietary nutrient availability

Previously a rich array of nitrogen fixation genes was discovered in Costa Rican Nasutitermes symbionts [6], and we also observed large numbers of genes encoding nitrogenase components in the laboratory N. corniger. However, some of these genes were strikingly underrepresented in the A. wheeleri hindgut metagenome, although still over-represented compared to other ecosystems, such as switchgrass-adapted compost [28] and farm soil [21]. Furthermore, higher gene expression of nitrogen fixation and ammonia assimilation pathways was also observed in N. corniger as compared to A. wheeleri. This suggests that in addition to fixing nitrogen, the A. wheeleri hindgut community utilizes fixed nitrogen from the diet. In cow dung, total nitrogen is usually 1–2% by dry weight [35], [36] and mainly present in organic forms (e.g. crude protein content of 6–10% [37]), in contrast to wood, which is extremely poor in nitrogen (e.g. total nitrogen as low as 0.05% [38]). Apart from the undigested lignocellulose, cow dung contains endogenous secretions (such as mucus) and microbial cells and debris (mainly bacterial cell walls) from the cattle gastrointestinal tract [39], or from microbial decomposers of dung after its release, all of which can be potential sources of combined nitrogen. Consistent with usage of such compounds, we observed a higher abundance of genes involved in the degradation and utilization of peptidoglycan, a major component of microbial cell walls, in A. wheeleri relative to N. corniger (Figure 2, Table S4). Similarly, the soil-feeding termite Cubitermes orthognathus has been shown to metabolize peptidoglycan and other microbial cell components at a higher rate than cellulose [40]. Soil, like dung, has higher levels of fixed nitrogen than typical lignocellulose termite diets [41]. In addition, a number of gene families involved in amino acid and oligopeptide transport had higher relative expression in A. wheeleri, as did genes associated with amino acid and amine oxidation (Figure 2). Correspondingly, biosynthesis pathways for a number of amino acids were underrepresented in A. wheeleri as compared to N. corniger, including valine, leucine, isoleucine, phenylalanine, tryptophan and tyrosine, several of which are abundant amino acids in cow dung [42].

Notably, one of the enriched phyla in A. wheeleri is Synergistetes, accounting for ∼8% of total bacterial 16S rRNA gene pyrotags, compared to 0.1% in N. corniger; its augmentation in A. wheeleri is also supported by metagenome and metatranscriptome data (Figure S1). The cultivated representatives of the Synergistetes phylum generate energy primarily through peptide/amino acid fermentation, and the majority of them cannot grow on carbohydrates [43]. The available Synergistetes genomes are particularly rich in genes for COG F, amino acid transport and metabolism (an average of ∼11% of genes belonging to COG F among all COG categories) compared to other bacterial phyla, especially Spirochaetes (5% for COG F) [44]. In addition, most Synergistetes genomes also lack nitrogenases for fixing nitrogen. Therefore the augmentation of Synergistetes in dung-feeding A. wheeleri is also consistent with the higher protein content in cow dung.

Further evidence reflecting the higher nitrogen content in the diet of A. wheeleri is a paucity of genes involved in the uric acid cycle. When dietary nitrogen becomes limiting, termite bacterial symbionts can recycle uric acid, a host metabolic waste, to generate ammonia for assimilation [45]. In our study, although neither uricase, nor other enzymes participating in the aerobic degradation of uric acid to urea was identified in any of the three metagenomes, ureases were found in the two Nasutitermes species and not in A. wheeleri (Figure 2). In addition, we observed higher abundance and expression of ammonium transporters in the laboratory N. corniger than in A. wheeleri (Figure 2). In model bacteria, ammonia transporters were expressed in response to nitrogen limitation, and the transportation was turned off when nitrogen is sufficient and cellular glutamine concentration is high [46], [47]. Therefore, the overrepresentation and higher expression of ammonia transporters in N. corniger also likely indicated dietary nitrogen limitation. Thus, hindgut microbiota may play a larger role in nitrogen metabolism in wood-feeding N. corniger than in dung-feeding A. wheeleri as suggested by the overrepresentation of diverse functions related to nitrogen metabolism in N. corniger.

The lower nutrient content of wood relative to cow dung was also reflected by the overrepresentation of vitamin biosynthesis pathways (particularly thiamine) in the N. corniger metagenome and metatranscriptome (Figure 2). Wood has a lower phosphorus content (∼0.01% [48]) than cow dung (e.g. 0.55% by dry weight [49]). The N. corniger hindgut had a higher abundance of alkaline phosphatase and polyphosphate kinase genes, and higher expression levels of these genes and high affinity phosphate transporters than A. wheeleri, consistent with phosphorus limitation in the wood-feeding gut environment. Taken together, the community functional differences associated with higher availabilities of nutrients may explain why some termite genera preferentially feed on cow dung despite the enrichment for more recalcitrant lignocellulosic components after passage through the cattle gastrointestinal tract. This feeding behavior plays an important role in facilitating nutrient recycling and fertilizing of top soil in (sub)tropical regions [9].


Hydrogen is a key fermentation product and intermediate in the termite hindgut microbial food chain [50], [51], and can be generated and consumed through nickel-iron (NiFe) hydrogenase or iron-only (FeFe) hydrogenase activities. NiFe hydrogenase sequences were largely absent from both the N. corniger metagenome and metatranscriptome, and were found at low abundance in the A. wheeleri metagenome and metatranscriptome. Based on sequence similarity, NiFe hydrogenases were identified as belonging to members of the Synergistetes and Deltaproteobacteria (Figure 3a), both of which were minor constituents in N. corniger and were augmented in A. wheeleri. The FeFe hydrogenase genes were abundant in all three termite metagenomes, although most abundant in N. corniger. Their transcripts were found in much higher levels relative to NiFe hydrogenase transcripts, but were not statistically different between the two termite metatranscriptomes. The protein sequence homology-based phylogenetic classification of the FeFe hydrogenase large subunits showed that these genes were predominantly contributed by Spirochaetes in N. corniger, in contrast to A. wheeleri, where the largest fraction of these genes was present in Firmicutes (Figure 3b).

Figure 3. Taxonomic assignment of nickel-iron (NiFe) hydrogenases (a), the large subunit of iron-only (FeFe) hydrogenases (b), formyl tetrahydrofolate synthase (FTHFS) (c), and glycoside hydrolases family 3 (GH3) (d) by MEGAN using Blastp results against the NR database.


Homoacetogenesis, CO2 reduction to form acetate, is the major H2 sink in wood-feeding termites compared to methanogenesis in some soil-feeders [52], [53]. Homoacetogenesis is also likely the major H2 sink for the dung-feeding A. wheeleri, as evidenced by the abundance of homoacetogenesis genes and paucity of Archaea and methanogenesis genes. It was previously observed that NADPH-dependent formate dehydrogenases, which catalyze the first step in synthesizing the methyl group of acetate, were rare in Costa Rican Nasutitermes sp. [6] conflicting with the high abundances of other genes in the homoacetogenesis pathway. One previously hypothesized explanation for this anomaly was that an alternative route was used to generate formate, such as through the reaction of pyruvate formate lyases [6]. This protein family was present in both A. wheeleri and N. corniger, at a higher abundance and expression level in the former (Figure 2). Alternatively, cysteine and selenocysteine variants of hydrogenase-linked formate dehydrogenases (FDH(H)) that use H2 instead of NADPH were identified in the genome of Treponema primitia, a termite gut isolate, and were suggested to be an adaptation to the H2-rich termite gut [54]. The FDH(H) genes were also detected in a number of wood-feeding termite and cockroach guts, suggesting the importance of FDH(H) in CO2-reductive acetogenesis in these gut environments [55]. Homologs of these formate dehydrogenases were present in both A. wheeleri and N. corniger metagenomes, and were more abundant in the A. wheeleri metagenome. In addition, H2-formate dehydrogenases and several other genes associated with this pathway were expressed at higher levels in A. wheeleri relative to N. corniger (Figure 2).

Formyl tetrahydrofolate synthase (FTHFS), a key enzyme in the homoacetogenic pathway, has been used as a functional and phylogenetic marker for homoacetogenic bacteria [1]. Protein sequence similarity based taxonomic assignment indicated the dominant FTHFS shifted from Spirochaetes in N. corniger to Firmicutes in A. wheeleri (Figure S3c). Similar shifts were also observed for hydrogenases as discussed earlier, and glycoside hydrolase family 3 (Figure 3d), an abundant GH family in termite metagenomes for oligosaccharide degradation. These data indicate that key functional niches in the termite hindgut (e.g. oligosaccharide degradation, hydrogen formation and homoacetogenesis) are maintained through the activities of phylogenetically distinct populations in A. wheeleri and N. corniger as has been previously noted for hydrogenases [56] and FTHFS [57] in a range of termite genera. Interestingly, it was previously suggested that termite symbiotic Spirochaetes may have acquired their CO2 reductive acetogenesis capability through lateral gene transfer from Firmicutes, although whether the gene transfer event occurred before or after Spirochaetes had initially become termite symbionts has not been determined [58]. In the study by Ottesen and Leadbetter [57], treponeme-like sequences dominated the FTHFS sequences recovered from wood-feeding cockroaches, lower and higher termites; contrasting to the abundance of Firmicutes FTHFS sequences from omnivorous cockroaches and higher termites with other feeding regimes, including litter and grass/soil. This also supports our hypothesis that host diet plays an important role in determining the population occupying a niche.

Cell motility and chemotaxis

Termite guts exhibit steep physicochemical gradients [2], [3], [4], [5], [22]. Motility enables termite microbial symbionts actively accessing their substrates and locating themselves at a physiochemical gradient thermodynamically favorable for their reactions. In our study, motility-related genes are overrepresented in both termite P3 metagenomes compared to averaged data from a range of other microbiomes (Figure S3) and were also highly expressed by both termite communities (Figure S5). Among the termites, we observed higher abundances of the cell motility and chemotaxis genes in N. corniger (Table S4). Particularly, a methyl-accepting chemotaxis protein (MCP) family was notably more abundant in N. corniger. As all major microbial phyla in both termites are motile, we hypothesize that the difference in cell motility and chemotaxis gene abundance primarily reflects community compositional differences, in particular, the relative abundance of Spirochaetes. Often known as spiral-shaped bacteria, Spirochaetes are excellent swimmers with a unique form of motility in highly viscous liquids, such as termite lumen fluid. Their sheathed flagella reside within the periplasmic space, and are triggered by methylation or demethylation of MCPs to rotate [59]. Their motility accelerates as media viscosity increases, while most bacteria with external flagella are slowed down or stopped [60]. This gives Spirochaetes an advantage in the highly viscous termite hindgut environment by allowing them to penetrate substrates and locate themselves at physicochemical gradients favorable for their biochemical reactions. A redundancy of chemotaxis genes [61] and numerous MCP genes [62] have been found in some Spirochaetes isolate genomes. Consistent with this observation, the majority of MCP genes identified in the present study were classified as belonging to Spirochaetes, even in A. wheeleri where Spirochaetes are not the most abundant phylum (Figure S8). This likely reflects a higher number of MCP genes per genome in the termite Spirochaetes populations relative to other motile bacterial populations and also accounts for the higher abundance of MCP genes in N. corniger metagenome (Table S4).

Adaptation to dung

Despite being a rich source of nutrients, cow dung may impose a competitive pressure on termite gut microbiota due to the presence of microbial cells and their products in ingested dung. Consistent with this idea is a higher abundance of defense genes in A. wheeleri than in N. corniger. Specifically, we observed a higher abundance and expression of genes in A. wheeleri associated with export of antimicrobial peptides (Figure 2, Tables S4 & S5), such as bacteriocin, generally found in Gram-positive bacteria for use against closely related bacteria. A. wheeleri P3 microbiota also exhibited higher abundances of genes involved in resistance to antimicrobial compounds (Table S4), probably indicating higher levels of these compounds in cow dung relative to wood, as antimicrobial compounds can be naturally produced by rumen symbionts (and obtained through cattle food additives to enhance beef production) [63].

However, on an evolutionary time scale, the microbial competitors present in dung may have also contributed to the hindgut paunch microbiota, either via lateral gene transfer, or whole organism transfer. Close relatives of some Firmicutes OTUs in A. wheeleri have been found in other anoxic environments, e.g. animal guts or dung (Figure 1). Firmicutes is the most abundant phylum in the cow rumen [31] and many Firmicutes species are able to form endospores. In line with this, we saw higher abundances of genes related to sporulation and germination in A. wheeleri (Table S4), and this capability may have allowed rumen-derived Firmicutes to survive passage through the termite gut facilitating colonization of the hindgut paunch and/or lateral gene transfer.

In summary, comparison of P3 lumen microbiota from two different genera of higher termites with different diets revealed distinct community and functional profiles strongly reflecting dietary differences, but also likely influenced by termite host phylogeny. Despite these differences, functions essential to termite biology were conserved in both P3 microbiomes. The relative contributions of host phylogeny and diet will need to be addressed by molecular analyses of additional termite taxa, including congeneric species with different feeding habits. The current study provides a useful baseline for such future work as the genera Nasutitermes and Amitermes both comprise numerous species with varied diets [9], [64].

Materials and Methods

Termite sources and DNA extraction

The Amitermes wheeleri sample was collected from a cow pie in the Arizona desert (31.533, −110.012, 4607 ft), primarily vegetated with creosote bushes and grasses. Nasutitermes corniger was obtained from a field-collected colony in Dania Beach, Florida, and was maintained in the laboratory with Schinus terebinthifolius (Brazilian pepper) wood and damp cellulose sponges. The field studies did not involve endangered or protected species, and no specific permits were required for the described field studies. The hindgut P3 segment of worker termites was incised, and disrupted with 23¼ gauge syringe needles. Approximately 10 µl of phosphate buffered saline (PBS, pH 7.2) was added to the disrupted P3 and mixed by repetitive pipetting. The PBS mixed luminal contents were aspirated into a pipette and pooled into a microcentrifuge tube. The luminal contents were pooled from ∼80 worker termites from A. wheeleri or N. corniger and DNA was extracted using Cetyltrimethyl Ammonium Bromide (CTAB)-containing lysis buffer, with bead beating in the presence of phenol/chloroform/isoamylalcohol, followed by phase separation and purification with polyethylene glycol (PEG), with details described elsewhere [28].

Pyrotag analysis of microbial community composition

Pyrotag sequencing of 16S rRNA amplicons was used for microbial community composition analysis. The experimental procedures were previously described [65]. The primer set 926F (5′-AAACTYAAAKGAATTGACGG-3′) and 1392R (5′-ACGGGCGGTGTGTRC-3′) which broadly targets Bacteria and Archaea was used to amplify the V8 regions of community 16S rRNA genes. The primers incorporated 454 adapters and barcodes so that they could be pooled and submitted for Roche 454 GS FLX sequencing. The sequences were analyzed using Pyrotagger ( and were quality trimmed to 200 bp, clustered into operational taxonomic units (OTUs) based on 97% sequence identity, and compared (blastn) against the Greengenes database for assigning taxonomic affiliation [66].

16S rRNA phylogenetic reconstruction

The OTUs comprising >0.5% of the total bacterial community in either of the termite hindgut paunch microbiota were identified as major OTUs. The representative (centroid) sequences for these OTUs were used to reconstruct a phylogenetic tree, using the quality-trimmed 200 bp from the V8 region amplified from the pyrotag analysis. Sequences were aligned using the SINA Webaligner ( and imported into the SILVA database (release 102) [67] using the ARB software package [68]. A backbone phylogenetic tree was constructed with over 1,000 full-length reference sequences using FastTree [69]. Short pyrotag reads were added to this tree according to maximum-parsimony criteria as provided by ARB. Finally, all reference sequences were removed without altering the calculated tree topology.

Metagenome sequencing, assembly and annotation

Sanger sequencing from a small insert (3 kb) library and 454 GS FLX Titanium sequencing were performed according to DOE Joint Genome Institute standard operating procedures for shotgun sequencing ( The 454 raw reads were submitted to the NCBI Short Read Archive (SRA) with Submission IDs of SRA057543 and SRA057337 for A. wheeleri and N. corniger metagenomes, respectively. Sequencing reads were quality trimmed using LUCY [70]. The quality trimmed Sanger and 454 reads were then combined and assembled using the Newbler assembler software (version 2.0, 454 Life Sciences), with a minimum overlap length (ml) of 30 and a minimum overlap identity (mi) of 0.95. The assemblies (including assembled contigs and unassembled singlets) were uploaded to the Integrated Microbial Genomes with Microbiome (IMG/M) system ( for gene calling with IMG/M Metagenome Gene Calling method and function annotation [71]. The annotated A. wheeleri and N. corniger metagenomes are publicly available with IMG/M Taxon Object IDs of 2030936000 and 2030936001 respectively. The average read depth for each contig was also uploaded to IMG/M as an estimator of the abundance of a population, from which the sequence was derived. The summary statistics for sequencing, assembly and annotation are shown in Table S1.

Metagenome comparison and statistical analysis

Gene-centric analysis was conducted to reveal the system commonalities and differences in function. We compared metagenomes based on the functional units of pfam, COG, COG pathways or functional categories. Results based on pfams were largely consistent with those derived from COGs. Therefore, the results were mostly interpreted based on COGs, and in some cases, results from pfams were also listed if corresponding COGs were not available (Table S4). In addition, a Costa Rican wood-feeding Nasutitermes metagenome [6] was included as an additional control to verify the differences observed between the cow dung-feeding A. wheeleri and the laboratory wood-feeding N. corniger. The abundance of each functional unit is the total count of genes belonging to that unit, adjusted (multiplied) by individual population abundance, which was estimated using the average read depth of the corresponding contig. Despite the difference in community composition, the average genome size was comparable between the two metagenomes (Table S7). Therefore, the genome size-associated bias should be minimal, and thus the abundance of individual functional units was compared based on the odds ratio between metagenomes. By the IMG/M abundance profile tool, Z-LOR is defined as the natural logarithm of odds ratio (LOR) divided by the standard error of LOR. Z-LOR was used to indicate the strength of difference between metagenomes, and P-values were calculated from Z-LOR, and adjusted for multiple hypothesis testing with a false discovery rate cutoff of 0.05 to identify statistically significant differences.

RNA extraction and cDNA Illumina library construction

Total RNA was extracted from the P3 segments pooled from ∼30 worker termites using the RiboPure™ Bacteria Kit (Ambion, Austin, TX) with DNase I digestion, following the manufacturer's instructions. The mRNA was enriched by using MICROBExpress™ Bacterial mRNA Enrichment Kit (Ambion). A chemical fragmentation step was performed by incubating the mRNA-enriched RNAs in 1× fragmentation solution (Ambion) at 70°C for 5 min to generate average fragment sizes of ∼175–200 bp. The fragmented RNAs were then used to synthesize double-stranded cDNAs using the SuperScript II Double-Stranded cDNA Synthesis Kit (Invitrogen, Carlsbad, CA), with random hexamers for the first strand synthesis and nick translation for the second strand synthesis. Illumina libraries were prepared by using the Illumina genomic sample prep kit (Illumina, San Diego, CA), according to manufacturer's instructions. A gel electrophoresis purification step was performed to select ligation products ranging from 200 to 500 bp, using the MiniElute Gel Extraction Kit (Qiagen, Valencia, CA). A PCR with 15 cycles using adaptor primers was performed to enrich adaptor-modified cDNA fragments. For each library, one lane of single-end 34 bp and one lane of paired-end 76 bp reads were sequenced on the Illumina Genome Analyzer (GA) II platform. In addition, one lane of paired-end 113 bp reads was generated on the Illumina GAIIx platform for N. corniger and a total of nine lanes (including failed lanes with low qualities) were generated for A. wheeleri microbiota due to its higher diversity. In total there were 11.4 Gbp of raw sequences generated for N. corniger and 62.4 Gbp for A. wheeleri (Table S3).

Metatranscriptomics bioinformatics

Due to the high microbial diversity of our samples, each metagenome (MG) was combined with its corresponding de novo assembled metatranscriptome (MT) to generate a more complete reference (MG+MT) for mapping cDNA reads for gene expression. Figure S4 summarized the analysis workflow. Briefly, to generate the metagenome-guided de novo metatranscriptome assembly (MT), a rapid rRNA filter using DUK, an efficient Kmer matching tool developed by JGI [72], was first applied to remove a large fraction of rRNA reads. Adapter and low quality sequences were trimmed using in-house tools and the overlapping paired reads were individually assembled using Phrap (, version 1.0.1). The assembled mate pairs and the remaining reads were then used for a de novo assembly with Velvet [73] to generate the initial metatranscriptome contigs, which were uploaded to IMG/M for annotation (Taxon Object ID is 2162886001 and 2162886000 for the de novo assembled A. wheeleri and N. corniger metatranscriptome respectively). These contigs and the predicted genes from corresponding metagenome were coassembled using Newbler (454 Life Sciences). The transcriptome reads were then aligned back to these contigs using BWA (version 1.2.2) [74] and the paired-read alignments were used to associate the reads into scaffold-groups. Each scaffold's reads were either iteratively assembled with Velvet using multiple, increasing kmers, or assembled with Newbler if the estimated read depth was 200 or less. This allowed each transcriptional unit to be assembled individually with accurate estimates of read coverage and insert size distribution and each assembly was sufficiently small to be performed on commodity hardware on the compute-cluster. These final metatranscriptome contigs (MT) were used for functional annotation by the IMG/M pipeline. Later analysis indicated that low-coverage transcripts were not adequately assembled into single contigs with this method, so the transcriptome contigs were assembled with the predicted genes from the metagenome assembly using Phrap to generate a more complete reference (MG+MT), followed by IMG/M functional annotation. Annotated coassemblies are publicly available, with Taxon Object IDs of 2228664018 and 2228664019 for A. wheeleri and N. corniger respectively. For gene expression levels, the quality-trimmed cDNA reads were mapped to MG+MT genes using BWA. As a considerable amount of cDNA reads were found to derive from eukaryotes, especially from the hosts, proteins that shared >30% similarities to eukaryote sequences were excluded from analysis. Median read coverage over each gene was used to indicate its expression. Expression levels of genes from the same COG or pfam were summed to perform comparative analysis based on COGs or pfams, where the expression of each function was compared by using its odds ratio between the two metatranscriptomes. Similar to the metagenome comparison, Z-LOR was used to indicate the strength of difference between metatranscriptomes, and P-values were calculated from Z-LOR, and adjusted for multiple hypothesis testing with a false discovery rate cutoff of 0.05 to identify statistically significant differences.

Supporting Information

Figure S1.

Microbial community diversity and phylogenetic composition. (a) Rarefaction curves labeled with the Shannon diversity index (H). (b, c, d, e) Bacterial community composition at the phylum level by 16S rRNA pyrotag (b), by metagenome genes from 38 IMG/M phylogenetic marker COGs (c), and by all genes with function prediction in the metagenomes (d) and in the de novo assembled metatranscriptomes (e), respectively. In (c, d, e), sequence homology-based classification was performed by Blastp against NR, and taxonomy was assigned by using the lowest common ancestor algorithm of MEGAN. Taxonomic distribution was then weighted by read depths or expression levels of individual genes for metagenomes or metatranscriptomes respectively.


Figure S2.

Hierarchical clustering of metagenomes based on similarities in pfam profiles. Proximity of grouping indicates the relative degree of similarity of samples to each other. The number of genes from each pfam was normalized by the total number of genes from all pfams found in each metagenome to generate the pfam profile. The number in the parenthesis is the metagenome Taxon Object ID in IMG/M.


Figure S3.

Natural logarithm of odds ratios of COG categories when comparing the three termite hindgut P3 metagenomes to the average of a total of 149 metagenomes not associated with termites (a) and to the average of all 2456 bacterial genomes (b) in the IMG/M database as publically available in March, 2011.


Figure S4.

Bioinformatic workflow of metatranscriptome analyses.


Figure S5.

Transcript distribution among COG categories.


Figure S6.

A cluster heatmap showing the clustering patterns of lignocellulose-degrading communities based on GH composition (the relative abundance of individual GHs in total GHs, based on gene counts). Only the GHs with the relative abundance ≥2% in any one of these microbiomes were shown in this heatmap.


Figure S7.

Relative abundance of each GH in the four categories of GHs listed above for metagenomes (a) and metatranscriptomes (b).


Figure S8.

Taxonomic distribution of methyl-accepting chemotaxis protein (MCP) family by MEGAN using Blastp results against the NR database.


Table S1.

Metagenome sequencing, assembly and annotation summary


Table S2.

Phylogenetic marker COGs used in phylogenetic distribution


Table S3.

Metatranscriptome sequencing, assembly, annotation and read mapping summary


Table S4.

Major functions differentially represented between A. wheeleri and Nasutitermes spp. Metagenomes


Table S5.

Major functions differentially expressed between A. wheeleri and N. corniger metatranscriptomes


Table S6.

Glycoside hydrolase (GH) abundance in metagenomes


Table S7.

Estimated average genome size in metagenomes using single-copy phylogenetic markers



We thank Anna Engelbrektson and Mary Ann Pedraza for valuable technical work, Steve Lowry and Amrita Pati for help in data analysis, Anna Engelbrektson, Victor Kunin and Lisa Margonelli for help in sample collection, and the Joint Genome Institute R&D and production teams for sequencing support. We also thank the anonymous reviewers for their careful reviews and helpful suggestions.

Author Contributions

Involved in discussion of the results and provided feedback on the manuscript: SH NI EK MA CB RHS NCK FW SGT PH.. Conceived and designed the experiments: PH FW NI NCK. Performed the experiments: FW MA CB RHS PH. Analyzed the data: SH NI EK MA. Wrote the paper: SH SGT PH.


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